Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

Guerra, Luis and McGarry, Laura M. and Robles Forcada, Víctor and Bielza, Concha and Larrañaga Múgica, Pedro and Yuste, Rafael (2010). Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study. "Developmental Neurobiology", v. 71 (n. 1); pp. 71-82. ISSN 1932-8451. https://doi.org/10.1002/dneu.20809.

Description

Title: Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study
Author/s:
  • Guerra, Luis
  • McGarry, Laura M.
  • Robles Forcada, Víctor
  • Bielza, Concha
  • Larrañaga Múgica, Pedro
  • Yuste, Rafael
Item Type: Article
Título de Revista/Publicación: Developmental Neurobiology
Date: December 2010
ISSN: 1932-8451
Volume: 71
Subjects:
Freetext Keywords: supervised;classification;clustering;pyramidal cell;interneuron
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies.

More information

Item ID: 7284
DC Identifier: http://oa.upm.es/7284/
OAI Identifier: oai:oa.upm.es:7284
DOI: 10.1002/dneu.20809
Official URL: http://onlinelibrary.wiley.com/doi/10.1002/dneu.20809/suppinfo
Deposited by: Memoria de Investigacion 2
Deposited on: 30 May 2011 09:14
Last Modified: 20 Apr 2016 16:27
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